文件列表:

CAE (0, 2013-09-08)
CAE\caeapplygrads.m (1219, 2013-09-08)
CAE\caebbp.m (917, 2013-09-08)
CAE\caebp.m (1011, 2013-09-08)
CAE\caedown.m (259, 2013-09-08)
CAE\caeexamples.m (764, 2013-09-08)
CAE\caenumgradcheck.m (3618, 2013-09-08)
CAE\caesdlm.m (845, 2013-09-08)
CAE\caetrain.m (1148, 2013-09-08)
CAE\caeup.m (489, 2013-09-08)
CAE\max3d.m (173, 2013-09-08)
CAE\scaesetup.m (1937, 2013-09-08)
CAE\scaetrain.m (270, 2013-09-08)
CNN (0, 2013-09-08)
CNN\cnnapplygrads.m (575, 2013-09-08)
CNN\cnnbp.m (2140, 2013-09-08)
CNN\cnnff.m (1774, 2013-09-08)
CNN\cnnnumgradcheck.m (3590, 2013-09-08)
CNN\cnnsetup.m (1765, 2013-09-08)
CNN\cnntest.m (193, 2013-09-08)
CNN\cnntrain.m (845, 2013-09-08)
DBN (0, 2013-09-08)
DBN\dbnsetup.m (557, 2013-09-08)
DBN\dbntrain.m (232, 2013-09-08)
DBN\dbnunfoldtonn.m (425, 2013-09-08)
DBN\rbmdown.m (90, 2013-09-08)
DBN\rbmtrain.m (1328, 2013-09-08)
DBN\rbmup.m (89, 2013-09-08)
LICENSE (1313, 2013-09-08)
NN (0, 2013-09-08)
NN\nnapplygrads.m (628, 2013-09-08)
NN\nnbp.m (1638, 2013-09-08)
NN\nnchecknumgrad.m (704, 2013-09-08)
NN\nneval.m (772, 2013-09-08)
NN\nnff.m (1849, 2013-09-08)
NN\nnpredict.m (188, 2013-09-08)
NN\nnsetup.m (1844, 2013-09-08)
NN\nntest.m (180, 2013-09-08)
NN\nntrain.m (2414, 2013-09-08)
... ...

DeepLearnToolbox ================ A Matlab toolbox for Deep Learning. Deep Learning is a new subfield of machine learning that focuses on learning deep hierarchical models of data. It is inspired by the human brain's apparent deep (layered, hierarchical) architecture. A good overview of the theory of Deep Learning theory is [Learning Deep Architectures for AI](http://www.iro.umontreal.ca/~bengioy/papers/ftml_book.pdf) For a more informal introduction, see the following videos by Geoffrey Hinton and Andrew Ng. * [The Next Generation of Neural Networks](http://www.youtube.com/watch?v=AyzOUbkUf3M) (Hinton, 2007) * [Recent Developments in Deep Learning](http://www.youtube.com/watch?v=VdIURAu1-aU) (Hinton, 2010) * [Unsupervised Feature Learning and Deep Learning](http://www.youtube.com/watch?v=ZmNOAtZIgIk) (Ng, 2011) If you use this toolbox in your research please cite [Prediction as a candidate for learning deep hierarchical models of data](http://www2.imm.dtu.dk/pubdb/views/publication_details.php?id=6284) ``` @MASTERSTHESIS\{IMM2012-06284, author = "R. B. Palm", title = "Prediction as a candidate for learning deep hierarchical models of data", year = "2012", } ``` Contact: rasmusbergpalm at gmail dot com Directories included in the toolbox ----------------------------------- `NN/` - A library for Feedforward Backpropagation Neural Networks `CNN/` - A library for Convolutional Neural Networks `DBN/` - A library for Deep Belief Networks `SAE/` - A library for Stacked Auto-Encoders `CAE/` - A library for Convolutional Auto-Encoders `util/` - Utility functions used by the libraries `data/` - Data used by the examples `tests/` - unit tests to verify toolbox is working For references on each library check REFS.md Setup ----- 1. Download. 2. addpath(genpath('DeepLearnToolbox')); Everything is work in progress ------------------------------ Example: Deep Belief Network --------------------- ```matlab function test_example_DBN load mnist_uint8; train_x = double(train_x) / 255; test_x = double(test_x) / 255; train_y = double(train_y); test_y = double(test_y); %% ex1 train a 100 hidden unit RBM and visualize its weights rng(0); dbn.sizes = [100]; opts.numepochs = 1; opts.batchsize = 100; opts.momentum = 0; opts.alpha = 1; dbn = dbnsetup(dbn, train_x, opts); dbn = dbntrain(dbn, train_x, opts); figure; visualize(dbn.rbm{1}.W'); % Visualize the RBM weights %% ex2 train a 100-100 hidden unit DBN and use its weights to initialize a NN rng(0); %train dbn dbn.sizes = [100 100]; opts.numepochs = 1; opts.batchsize = 100; opts.momentum = 0; opts.alpha = 1; dbn = dbnsetup(dbn, train_x, opts); dbn = dbntrain(dbn, train_x, opts); %unfold dbn to nn nn = dbnunfoldtonn(dbn, 10); nn.activation_function = 'sigm'; %train nn opts.numepochs = 1; opts.batchsize = 100; nn = nntrain(nn, train_x, train_y, opts); [er, bad] = nntest(nn, test_x, test_y); assert(er < 0.10, 'Too big error'); ``` Example: Stacked Auto-Encoders --------------------- ```matlab function test_example_SAE load mnist_uint8; train_x = double(train_x)/255; test_x = double(test_x)/255; train_y = double(train_y); test_y = double(test_y); %% ex1 train a 100 hidden unit SDAE and use it to initialize a FFNN % Setup and train a stacked denoising autoencoder (SDAE) rng(0); sae = saesetup([784 100]); sae.ae{1}.activation_function = 'sigm'; sae.ae{1}.learningRate = 1; sae.ae{1}.inputZeroMaskedFraction = 0.5; opts.numepochs = 1; opts.batchsize = 100; sae = saetrain(sae, train_x, opts); visualize(sae.ae{1}.W{1}(:,2:end)') % Use the SDAE to initialize a FFNN nn = nnsetup([784 100 10]); nn.activation_function = 'sigm'; nn.learningRate = 1; nn.W{1} = sae.ae{1}.W{1}; % Train the FFNN opts.numepochs = 1; opts.batchsize = 100; nn = nntrain(nn, train_x, train_y, opts); [er, bad] = nntest(nn, test_x, test_y); assert(er < 0.16, 'Too big error'); %% ex2 train a 100-100 hidden unit SDAE and use it to initialize a FFNN % Setup and train a stacked denoising autoencoder (SDAE) rng(0); sae = saesetup([784 100 100]); sae.ae{1}.activation_function = 'sigm'; sae.ae{1}.learningRate = 1; sae.ae{1}.inputZeroMaskedFraction = 0.5; sae.ae{2}.activation_function = 'sigm'; sae.ae{2}.learningRate = 1; sae.ae{2}.inputZeroMaskedFraction = 0.5; opts.numepochs = 1; opts.batchsize = 100; sae = saetrain(sae, train_x, opts); visualize(sae.ae{1}.W{1}(:,2:end)') % Use the SDAE to initialize a FFNN nn = nnsetup([784 100 100 10]); nn.activation_function = 'sigm'; nn.learningRate = 1; %add pretrained weights nn.W{1} = sae.ae{1}.W{1}; nn.W{2} = sae.ae{2}.W{1}; % Train the FFNN opts.numepochs = 1; opts.batchsize = 100; nn = nntrain(nn, train_x, train_y, opts); [er, bad] = nntest(nn, test_x, test_y); assert(er < 0.1, 'Too big error'); ``` Example: Convolutional Neural Nets --------------------- ```matlab function test_example_CNN load mnist_uint8; train_x = double(reshape(train_x',28,28,60000))/255; test_x = double(reshape(test_x',28,28,10000))/255; train_y = double(train_y'); test_y = double(test_y'); %% ex1 Train a 6c-2s-12c-2s Convolutional neural network %will run 1 epoch in about 200 second and get around 11% error. %With 100 epochs you'll get around 1.2% error rng(0) cnn.layers = { struct('type', 'i') %input layer struct('type', 'c', 'outputmaps', 6, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %sub sampling layer struct('type', 'c', 'outputmaps', 12, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %subsampling layer }; cnn = cnnsetup(cnn, train_x, train_y); opts.alpha = 1; opts.batchsize = 50; opts.numepochs = 1; cnn = cnntrain(cnn, train_x, train_y, opts); [er, bad] = cnntest(cnn, test_x, test_y); %plot mean squared error figure; plot(cnn.rL); assert(er<0.12, 'Too big error'); ``` Example: Neural Networks --------------------- ```matlab function test_example_NN load mnist_uint8; train_x = double(train_x) / 255; test_x = double(test_x) / 255; train_y = double(train_y); test_y = double(test_y); % normalize [train_x, mu, sigma] = zscore(train_x); test_x = normalize(test_x, mu, sigma); %% ex1 vanilla neural net rng(0); nn = nnsetup([784 100 10]); opts.numepochs = 1; % Number of full sweeps through data opts.batchsize = 100; % Take a mean gradient step over this many samples [nn, L] = nntrain(nn, train_x, train_y, opts); [er, bad] = nntest(nn, test_x, test_y); assert(er < 0.08, 'Too big error'); % Make an artificial one and verify that we can predict it x = zeros(1,28,28); x(:, 14:15, 6:22) = 1; x = reshape(x,1,28^2); figure; visualize(x'); predicted = nnpredict(nn,x)-1; assert(predicted == 1); %% ex2 neural net with L2 weight decay rng(0); nn = nnsetup([784 100 10]); nn.weightPenaltyL2 = 1e-4; % L2 weight decay opts.numepochs = 1; % Number of full sweeps through data opts.batchsize = 100; % Take a mean gradient step over this many samples nn = nntrain(nn, train_x, train_y, opts); [er, bad] = nntest(nn, test_x, test_y); assert(er < 0.1, 'Too big error'); %% ex3 neural net with dropout rng(0); nn = nnsetup([784 100 10]); nn.dropoutFraction = 0.5; % Dropout fraction opts.numepochs = 1; % Number of full sweeps through data opts.batchsize = 100; % Take a mean gradient step over this many samples nn = nntrain(nn, train_x, train_y, opts); [er, bad] = nntest(nn, test_x, test_y); assert(er < 0.1, 'Too big error'); %% ex4 neural net with sigmoid activation function rng(0); nn = nnsetup([784 100 10]); nn.activation_function = 'sigm'; % Sigmoid activation function nn.learningRate = 1; % Sigm require a lower learning rate opts.numepochs = 1; % Number of full sweeps through data opts.batchsize = 100; % Take a mean gradient step over this many samples nn = nntrain(nn, train_x, train_y, opts); [er, bad] = nntest(nn, test_x, test_y); assert(er < 0.1, 'Too big error'); %% ex5 plotting functionality rng(0); nn = nnsetup([784 20 10]); opts.numepochs = 5; % Number of full sweeps through data nn.output = 'softmax'; % use softmax output opts.batchsize = 1000; % Take a mean gradient step over this many samples opts.plot = 1; % enable plotting nn = nntrain(nn, train_x, train_y, opts); [er, bad] = nntest(nn, test_x, test_y); assert(er < 0.1, 'Too big error'); %% ex6 neural net with sigmoid activation and plotting of validation and training error % split training data into training and validation data vx = train_x(1:10000,:); tx = train_x(10001:end,:); vy = train_y(1:10000,:); ty = train_y(10001:end,:); rng(0); nn = nnsetup([784 20 10]); nn.output = 'softmax'; % use softmax output opts.numepochs = 5; % Number of full sweeps through data opts.batchsize = 1000; % Take a mean gradient step over this many samples opts.plot = 1; % enable plotting nn = nntrain(nn, tx, ty, opts, vx, vy); % nntrain takes validation set as last two arguments (optionally) [er, bad] = nntest(nn, test_x, test_y); assert(er < 0.1, 'Too big error'); ```

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